Abstract : Image segmentation is a crucial operation for image processing. It is always the starting point of shape analysis process , motion detection, visualization, and quantitative estimates of linear distances, surfaces and volumes. For this, the segmentation is to classify the voxels into classes based on their local strengths, their spatial location and shape characteristics or neighborhood. The difficulty of the stability of the results of segmentation methods for medical images come from different types of noise present. In these images, the noise takes two forms: physical noise due to the acquisition system, in our case, MRI Magnetic Resonance Imaging, and physiological noise due to the patient. These noises should be considered for all methods of segmentation. In this thesis, we focused on Multi-Agent models based on the biological behavior of spiders and ants to perform the task of segmentation. For spiders, we proposed a semi-automatic method using the histogram of the image to determine the number of objects to be detected. As for ants, we proposed two approaches: one that uses the so-called classical gradient of the image and the second, more original, which uses an intervoxel partition of the image. We also proposed a way to speed up the segmentation process through the use of the GPU Graphics Processing Unit. Finally, these two methods were evaluated on MR images of brain and were compared with conventional methods of segmentation: region growing and Otsu for the model of spiders and Sobel gradient for the ants.